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ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India 被引量:6
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作者 farhan mohammad khan Rajiv Gupta 《Journal of Safety Science and Resilience》 2020年第1期12-18,共7页
In this paper,we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India.We adopted an Auto-Regressive Integrated Moving Average(AR... In this paper,we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India.We adopted an Auto-Regressive Integrated Moving Average(ARIMA)model on the data collected from 31st January 2020 to 25th March 2020 and verified it using the data collected from 26th March 2020 to 04th April 2020.A nonlinear autoregressive(NAR)neural network was developed to compare the accuracy of predicted models.The model has been used for daily prediction of COVID-19 cases for next 50 days without any additional intervention.Statistics from various sources,including the Ministry of Health and Family Welfare(MoHFW)and http://covid19india.org/are used for the study.The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases with approximately 1500 cases per day,based on available data as on 04th April 2020.The appropriate ARIMA(1,1,0)model was selected based on the Bayesian Information Criteria(BIC)values and the overall highest R 2 values of 0.95.The NAR model architecture constitutes ten neurons,which was optimized using the Levenberg-Marquardt optimization training algorithm(LM)with the overall highest R 2 values of 0.97. 展开更多
关键词 Time series Novel coronavirus SARS-CoV-2 Forecasting ARIMA NAR INDIA
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Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods 被引量:2
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作者 farhan mohammad khan Akshay Kumar +2 位作者 Harish Puppala Gaurav Kumar Rajiv Gupta 《Journal of Safety Science and Resilience》 CSCD 2021年第2期50-62,共13页
There is a new public health catastrophe forbidding the world.With the advent and spread of 2019 novel coro-navirus(2019-nCoV).Learning from the experiences of various countries and the World Health Organization(WHO)g... There is a new public health catastrophe forbidding the world.With the advent and spread of 2019 novel coro-navirus(2019-nCoV).Learning from the experiences of various countries and the World Health Organization(WHO)guidelines,social distancing,use of sanitizers,thermal screening,quarantining,and provision of lock-down in the cities being the effective measure that can contain the spread of the pandemic.Though complete lockdown helps in containing the spread,it generates complexity by breaking the economic activity chain.Besides,laborers,farmers,and workers may lose their daily earnings.Owing to these detrimental effects,the government has to open the lockdown strategically.Prediction of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a strategy.An attempt is made in this paper to predict the time after which the number of new cases stops rising,considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree,Support Vector Machine,and Gaussian Process Regression algorithm are used to project the number of cases.Thus,the projections are used in identifying inflection points,which would help in planning the easing of lockdown in a few of the areas strategically.The criticality in a region is evaluated using the criticality index(CI),which is proposed by authors in one of the past of research works.This research work is made available in a dashboard to enable the decision-makers to combat the pandemic. 展开更多
关键词 COVID-19 Machine learning TRANSMISSION Lockdown Gaussian process regression Support vector machine Decision tree
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